Portable composable machine vision system for identifying objects for recycling purposes

ABSTRACT

A machine vision system for automatically identifying and inspecting objects is disclosed, including composable vision-based recognition modules and a decision algorithm to perform the final determination on object type and quality. This vision system has been used to develop a Projectile Identification System and an Automated Tactical Ammunition Classification System. The technology can be used to create numerous other inspection and automated identification systems.

REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/675,763, filed Aug. 13, 2017, which is a continuation of U.S. patentapplication Ser. No. 14/638,313, filed Mar. 4, 2015, now U.S. Pat. No.9,734,569, which is a continuation of U.S. patent application Ser. No.14/163,602, filed Jan. 24, 2014, now U.S. Pat. No. 8,983,173, which is acontinuation of U.S. patent application Ser. No. 11/072,599, filed Mar.4, 2005, now abandoned, which claims priority from U.S. ProvisionalPatent Application Ser. No. 60/550,188, filed Mar. 4, 2004, the entirecontent of each of the above being incorporated herein by reference.

FIELD OF THE INVENTION

This invention relates to automated inspection and, in particular, to acomposable machine-vision system for identifying and inspectingordnance, ammunition, and other objects.

BACKGROUND OF THE INVENTION

The ability to automatically identify and inspect objects is importantfor controlling manufacturing processes, automating processes, andreducing tedious tasks that must be performed by humans. Specializedapplications-specific machine vision systems have been historicallyemployed for such systems.

U.S. Pat. No. 4,163,212 to Buerger et al. describes a patternrecognition system which was designed in the late 1970's that used videoimagers to recognize the position and orientation of an integratedcircuit so as to control a wire bonding machine operation. U.S. Pat. No.6,748,104 to Bachelder et al. describes a similar system that identifiesthe position of semiconductor and inspects it based on correlationbetween images and model patterns (edges, corners or other templates).

U.S. Pat. Nos. 4,696,047 and 4,589,141 to Christian et al. describesystems which were built beginning in the early 1980's that usedcomputer vision-based inspection technology for dedicated inspectionapplications (U.S. Pat. No. 4,696,047, inspection of electricalconnectors and U.S. Pat. No. 4,589,141, inspection of printed labels).U.S. Pat. No. 4,706,120 to Slaughter et al. describes a modular visionsystem built in the early 1980s that was based on earlier ones built bysome of the inventors of the system that is the subject of this patentdisclosure. It supported various dedicated inspection applications likethose previous described. At this time, modular meant that the systemcould be included in a larger system as a module.

U.S. Pat. Nos. 5,142,591 and 5,157,486 to Baird et al. describe a systemfor imaging the silhouette of an ammunition object using a line scancamera and counter to reduce data rate to a microprocessor thatimplements silhouette boundary inspection of the object as it moves downthe conveyer. U.S. Pat. No. 5,311,977 to Dean et al. describes a similarsystem that singulates objects on a conveyor system and images themusing a high-resolution line scan CCD camera. Object images areconverted via a camera synchronized counter to a silhouette are comparedto reference silhouettes to effect inspection. These disclosures wereless focused on the boundary-based inspection algorithm and more onemploying specialized pre-processor counter hardware to reduce thecomputation expense of finding boundary edges in the line scan cameraoutput serial stream.

U.S. Pat. No. 5,608,530 to Gates describes a system for acquiring anobject silhouette by employing a laser backlight and measurement of theunobstructed portion of radiation-which has passed the radially opposedhalves of the part under measurement. General Inspection, Inc hasapplied this sensor approach to ammunition inspection and screwinspection. U.S. Pat. No. 5,978,502 to Ohashi describes a system thatinspects objects like solder bumps (on a printed circuit card) bycomparing range data measured by a sensor to range data representing agood part

U.S. Pat. Nos. 6,040,900 and 6,043,870 to Chen described laser-basedimaging system that use sherography and interferometry to form images ofprecision surface smoothness variations which are related to materialsdefects in composite materials. U.S. Pat. No. 6,122,001 to Micaletti etal. describes a system that uses laser illumination imaged through acamera system to triangulate the top of packages, which is then used tofocus a camera for the purpose of reading package addresses andultimately automating package sorting.

U.S. Pat. No. 6,448,549 to Safaee-Rad describes a bottle inspectionsystem that determines the quality of threads by capturing a videoimage, finding the bottleneck, and then assessing thread quality byanalyzing the white/dark texture pattern to determine if they resemblebottle threads. U.S. Pat. No. 6,584,805 to Burns et al. describes ainspection machine that extracts simple features from the image of abottle such as bottle diameter to inspect bottle just after hot molding.U.S. Pat. No. 6,618,495 to Funas describes an inspection machine forback-lit transparent containers that uses a camera to capture an imagewhich is compared by computer to a good container template image (meansfor defining on said illumination area light intensities varying betweenthe extremes of black and a maximum brightness level on said lightsource illumination area).

U.S. Pat. No. 6,801,637 to Voronka et al. describes a specializedcomputer vision system that tracks active light emitters in three linecameras to acquire movement of multiple body positions. The position ofeach emitter on the body is located through triangulation based on thewhere the emitter falls along each of the three linear cameras. Thesystem is calibrated by moving a selected light emitter to one orseveral known position in the movement measurement volume. Onecalibrated during manufacturing the system retains calibrationindefinitely. U.S. Pat. No. 6,831,996 to Williams et al. describes anapparatus that inspects automobile wheels using illumination and a zoomcontrol camera system that acquires wheel reference features (as anexample given the milled hole for a valve stem) to determine orientationand then performs inspection by assessing whether the features are inthe correct position.

Comparing image derived features to model features expressed attwo-dimensional patterns or boundaries has been done in both two andthree dimensions for defect detection. However, generally thesealgorithms have been development specifically for part handling orspecific part inspection. U.S. Pat. No. 6,173,066 to Peurach et al.describes a vision processing system that uses a specific approach topattern recognition of three-dimensional objects or parts from CAD-typetemplates, matched in multiple views. This system does no initiallyunderstand what it is likely to see or in what particular orientation soit describes a staged approach, which hypothesizes object, position andorientation, and follows this up with boundary oriented matchingprocedure between edges acquired from 3 dimensional images and 3Dboundaries defined by the 3D CAD-template. The cameras that take theobject images are calibrated through recognition of a calibration objectof known shape and size. One calibrated during manufacturing the systemretains calibration indefinitely.

U.S. Pat. No. 6,687,398 to Kriwet et al. discloses a method and devicefor the identification of incorrectly orientated parts and/or partsdeparting from a predetermined master, the parts being moved by means ofa conveyor means past at least one camera for registering the shapes ofthe parts. U.S. Pat. No. 6,822,181 to Linton describes a part divertersystem which might work with a system like Peurach or Kriwet. Hedescribes the use of an actuated paddle to divert an object from aninspection stream (pathway on a conveyor).

U.S. Pat. No. 6,714,671 to Wakitani et al. describes a system that usesmodel boundary matching to image derived boundaries for inspection ofwiring patterns of semiconductors, printed circuit boards, orprinted/impressed patterns. U.S. Pat. No. 6,714,679 to Scola et al.describes a boundary analysis technique that determines defects of aboundary to sub-pixel precision and an embodiment for fast correlationscoring for this technique. U.S. Pat. No. 6,856,698 to Silver et al.describes a pattern matching approach that compare model boundary pointswith edges extracted from imagers.

The prior art demonstrates that:

-   -   (1) Computer-vision-based boundary and pattern analysis for        inspection has been done since the 1970s.    -   (2) Prior systems have been specialize to particular inspections        to be performed, using special illumination (for instance back        lighting and laser illumination), and    -   (3) Prior systems have, for the most part, been focused on        applications where high speed operation is not combined with        generality or precision measurement.

SUMMARY OF THE INVENTION

This invention recognizes that advances in computer speed, imagingtechnology, and image processing technology now allow implementation ofa truly composable vision-based inspection system. This inventionfurther makes use of plug-in recognition modules and decision algorithmsto perform the final determination on object type and quality. Theseadvances are coupled with a modular, composable parts feeder and sortertechnology enabling a high-speed system for system for identifying andinspecting ordnance, ammunition, and other objects.

The preferred embodiment includes a computer-vision system with multiplecameras for simultaneously imaging opposite sides of an object underinspection and an image processor for analyzing the imaging anddetermining physical characteristics of an object under investigation.The computer-vision system preferably uses one or more line-scan camerasand an image processor operative to determine an object's speed andrecreate images of an object from the line data based upon the speed.

The feeder may include a bore through which an operator loads ordnance,wheels or sprockets configured to receive belt ammunition, or a bin toreceive bulk ammunition in a range of sizes. In the case where thefeeder is configured to receive loose objects in a range of sizes, amechanism is included for forming a single line or column of objects forpresentation to the computer-vision system. Such a mechanism may includea narrowing channel and restriction plates to form a single line orcolumn of bullets or other objects. If the is configured to receive bulkammunition in a range of calibers, a mechanism may further be providedfor separating bullets as a function of caliber. The sorter may includea blow-off unit to divert objects found to be unacceptable, or thesorter may be manually operated.

The system architecture is ‘composable’ in the sense that it allows forthe easy addition and replacement of different objectrecognition/identification modules. This increases the usefulness of thesystem to a variety of inspection areas. Thus, the image processor maybe operative to determine the color or hue of an object, intensity, oroperative to recognize alphanumerical characters, bar codes, orradio-frequency ID tags. The image processor may further be operative torecognize imperfections associated with dents or corrosion, includingcorrosion due to copper sulfate leakage as might be associated withdefective munitions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a drawing of a PIDS Manual Feed System according to theinvention;

FIG. 2 shows a Sprocket Belt Fed ATACS System;

FIG. 3 is a drawing of an Automated Bulk Feed ATACS System;

FIG. 4 depicts an Elevator Belt Scoop Ammunition from the InputReservoir;

FIG. 5A illustrates a Vibratory Feeder V-Channel and Restriction Plates;

FIG. 5B shows a Vibratory Feeder V-Channel for 50 Cal separation;

FIG. 6 is a drawing of a Guide roller;

FIG. 7 depicts a V-channel and sensors in the identification andinspection module;

FIG. 8 illustrates an Image assembly;

FIG. 9 shows a PIDS camera arrangement;

FIG. 10 is a drawing of an ATACS camera arrangement;

FIG. 11 depicts a Composable Machine Vision System Block Diagram;

FIG. 12 illustrates an ATACS bulk ammunition sorter/diverter;

FIG. 13 shows a Composable Identification Framework Block Diagram;

FIG. 14 depicts Sobel Operator Edge Detection;

FIG. 15 is a drawing of a Color Coding on Bullet Tip;

FIG. 16 depicts a Color-based Corrosion;

FIG. 17 illustrates a Nonstandard casing or bullet materials;

FIG. 18 shows Text Markings; and

FIG. 19 is a drawing that depicts Shoulder and dent gradients.

DETAILED DESCRIPTION OF THE INVENTION

The preferred embodiment of this invention incorporates the followingkey components:

-   -   (1) Flexible feeder    -   (2) General-purpose, composable computer-vision-based inspection        hardware module    -   (3) Flexible sorter/diverter

Important aspects of the composable vision system include:

-   -   (1) Flexible, multiple-view capture imaging system/camera        interfaces    -   (2) Plug-in vision processing modules for:        -   a. Calibration        -   b. Color/Hue processing & inspection        -   c. Boundary finding, scaling and comparison        -   d. Connected component processing & sizing        -   e. Shape-from-shading and dent detection        -   f. Area scanning and corrosion defect finding        -   g. Text-based OCR        -   h. RFID and/or Bar code processing        -   i. Flexible multidimensional decision making        -   j. Multiple models        -   k. Automated model capture/learning    -   (3) GUI-based vision module composition, control and decision        parameter tuning

The architecture for this system is ‘composable’ in the sense that itallows for the easy addition and replacement of recognition modules.This increases the usefulness of the system to a variety of inspectionareas. The following embodiment will be described with reference toProjectile Identification and Small Arms Ammunition Inspection. Both ofthese embodiments are applicable to use in ammunition manufacturingplants for in-process or end-process inspection. Other applicationsinclude machined parts inspection, recycling, entry/exit monitoring, andother uses apparent to those of skill in the relevant art(s).

Projectile Identification. This application is used for identifyingprojectiles as they are loaded into the magazine of automated fireweapons. The Projectile Identification System (PIDS) provides projectileidentification capability based on size, shape, color patterns, and textmarkings. It is hand or manually loaded and interfaces with the weaponsloading system through a communication link to transfer projectileidentification data. The PIDS preferably uses four (4) image sensorsthat can view all sides of an ordnance (with general purpose PC-basedvision processing hardware) and plug-in vision software algorithms.

The PIDS utilizes 5 recognition modules and a decision module toidentify 120 mm mortar shells as they are loaded into an automatedweapons system. These recognition modules include a shape detectionmodule, a hue-based color-matching module, an average intensity module,a character recognition module, and a specialized charge recognitionmodule (for determining the number of charges on the shell). Thedecision module fuses the information from each of these modules toidentify the type of round being loaded.

Small Arms Ammunition Inspection. This embodiment is used for sortingand inspecting small arms ammunition in a high-speed, automated manner.The Automated Tactical Ammunition Classification and System (ATACS) cansort and inspect up to 200 rounds per minute. The first stage is aflexible ammunition feeder system for belt or bulk ammunition. Thesecond stage is a high speed four (4) camera imaging/inspection unitthat identifies round based on size, color, shape, and end markings. Itis capable of determining chambering dimensions to approximately 0.002of an inch, detecting of surface or area defects (including dents andcorrosion), and determining round type. The system sorts the ammunitioninto individual bins based on color coding, type and quality.

The ATACS uses 5 recognition modules and a decision module to identifyand inspect small caliber ammunition or similar cylindrical objects.These modules include a length determination module, a shape/damagedetection module, a hue-based tip color recognition module, agradient-based shoulder dent detection algorithm, and a hue-basedcorrosion detection algorithm. The decision module fuses the resultsfrom the recognition modules and determines the type of cartridge (e.g.45 cal, 5.56 mm, 9 mm, 7.62 mm, etc.), the model number (e.g. M855,M1911, etc.), and whether or not the cartridge is defective. This systemis designed to work with ammunition returning from the field. As such,the ammunition to be inspected differs greatly in appearance.

Other uses for the system include:

Machined Parts Inspection. The technology described can be used to buildsmall, high-speed, general purpose inspection systems for machined partidentification, orientation determination, and dimensional/surfacequality/color inspection. The technology was originally designed toinspect axially symmetric parts, but can be used to inspect any objectif the part is presented in a known or near know orientation. Fullythree-dimensional inspection is possible by including the algorithmsdisclosed in U.S. Pat. No. 6,173,066 to Peurach as stages in thecomposable inspection plug-in list.

Recycling. The technology can be used to identify objects for recyclingpurposes. For example, plastic and/or glass bottles with surfacemarkings, possible defects, and specific shapes are nearly identical tolarger ordnance like those for which the PIDS system has been made.Based on their physical characteristics which include text markings,barcodes, color, shape, and size, recycle objects can automatically besorted out of a trash stream by recyclable object type (plastics, cans,boxes, glass, etc.)

Entry/Exit Monitoring. The technology can be used in vision systems thatanalyze object moving through a point of entry/exit. RFID and/or barcodetags are commonly used to identify commercial or logistical items.However, they require that the tag object be presented to the reader (inthe case of barcode, to the reader view port; in the case of RFID,within range of an RF reader). In the case of active tags like RFID, thetag must be functional to operate. This technology augments theseidentification means with added data derived from object size, shape,color (and markings), and text. This added data can provide a redundancycheck for RFID or barcode, and can provide a means for detectingpackages entering or leaving through the entryway even if the RFID orbarcode read fails to operate due to occlusion or malfunction. Such anentryway might be place at the loading point into or out of building orat the loading point for ISO transport containers to detectingunauthorized removal or placement of objects inside a the restrictedarea or ISO container.

FIG. 1 shows a manual feeder system for the Projectile IdentificationSystem (PIDS). This feeder is simply a portal through which an operatorloads ordnance. As the ordnance is loaded, it passes through thecomposable machine vision system and is identified and inspected. Anyordnance size that fits through the portal is acceptable for manualfeeding.

FIG. 2 shows an Automated Tactical Ammunition Classification and System(ATACS) for feeding belt ammunition. The belt is inserted into thefeeder sprockets and is pulled through the composable machine visionsystem controlled by the gear motor assembly that drives the sprocketwheel. The position of the ammunition to be inspected under the machinevision sensors is known based on rotation measured by the encoder unitsattached to the sprocket (or alternatively the drive motor). Ammunitionsize changes are accommodated by changing the sprocket wheel sizes.

FIG. 3 shows an ATACS automated feeder for feeding bulk ammunition of arange of sizes. The bulk ammunition is placed into the input reservoir.An elevator conveyor moves through the input reservoir at a steep angleand the protruding portions on the belt scoop up a lot of ammunition(without specific selection of any particular type or size—FIG. 4). Theelevator moves the lot of ammunition up and over the end, dropping itinto a fall chute that ends in a short v-channel (FIG. 5). Theammunition that is caught by the v-channel moves down a slight incline,speed regulated by regular adjustable vibrations of this channel.

The channel narrows and has restriction plates attached so that only asingle line or column of bullets can move down the line to the point ofentry into the composable machine vision inspection stage. Rounds thatdo not singulate into a single file fall into a conveyor belt that takesthem back to the input reservoir for re-feeding. Trial and error haveshown the inventors that the particular shape and number of therestriction plates shown in FIG. 5 are necessary to prevent feedingmultiple bullets into the inspection stage at or nearly at the sametime. Because 50 Cal and smaller rounds feed substantially differently,a v-channel has been designed which sorts 50 Cal separately from smallercaliber. For the arrangement shown in FIG. 6, the 50 Cal move down thechannel for inspection and the smaller caliber rounds fall through aslit in the bottom of the v-channel into a re-sort bin.

The machine vision system can use any of a variety of means for formingimages of the objects to be inspected. Single CCD cameras for imagingone side of an object from a single view point is the lowest cost andeasiest approach. However, the systems shown in FIGS. 1-3 image objectson all sides simultaneously so that full surface inspection is possible.Use of area cameras is possible for some lower resolution applications,however, the systems shown in the figures, used higher resolution linecameras (2000 color pixels or greater) for precision measurement. Toform an image, therefore, it is necessary to move the object through anactive imaging area at a known rate of progression so that the twodimensional image can be reassembled from multiple line images.

For the system in FIG. 1, this is accomplished by measuring therevolution of a guide roller upon which the ordnance under inspectionmoves as it is inserted through the inspection mode (FIG. 6). For thesystem in FIG. 2, measuring the rotation of the feed sprockets controlsthe ammunition feed rate. This provides the measurements needed toassemble ammunition images in this case. In FIG. 3 the drop length,velocity, and acceleration of an ammunition piece are measure by fourLED or laser detector devices.

Referring to FIG. 7, the bullet moves off of the vibrating inclinev-channel and passes through the first LED or laser detector. Thisdetection informs the controller that a bullet is in the inspectioninclined v-channel and can control the vibration stage to control thebullet feed rate. This v-channel is a hardened slippery material withgood wear characteristics so that the bullet moves smoothly down andaccelerates at a predictable rate, which is a fraction of theacceleration due to gravity. The bullet then passes through the threedetectors located close to the imaging sensor point. These threemeasurements provide the data needed to precisely measure the roundslength, speed upon exiting past the last detector and the round'sacceleration. Time taken for the bullet to fall by each of the detectorsprovides an estimate of bullet length (assuming an estimate of thevelocity and acceleration). Time take for the leading or trailing edgeof the bullet to fall from point T1 to T2 or point T2 to T3 provides anestimate of the velocity of the bullet assuming (assuming that theacceleration is known). And the difference in time betweenleading/trailing edge falling from T1 to T2 compared to T2 to T3provides and estimate of acceleration. These parameters are assumed tobe approximately correct (with speed adjusted based on measuredacceleration) as the round drops through the image capture point.

The line scan imaging sensors capture an image line-by-line for allthree feeder approaches which are assembled into multiple color 2Dimages comparable to those from conventional area scan cameras of highresolution (FIG. 8).

In the embodiments shown in FIG. 9, four (4) cameras are used to achievethe desired aerial coverage and resolution, however, more or fewercameras could be included at the imaging stage. In all of the systems,uniform ring illumination is used so that all cameras can be active atthe same time. However, in some systems (for instance FIG. 2) fewercameras might be used with backlighting or front lighting to enhance theobject features that are to be measured (ring illumination emphasizesall areas, front side illumination emphasizes object edges and frontfacing surfaces and surface texture, and backlighting emphasizes edgesor silhouettes only).

FIG. 10 shows a variation of the camera arrangement of FIG. 8 that isused for the ATACS ammunition inspection system. The PIDS inspectionsystem arrangement shown in FIG. 8 provides for uniform aerial coverageso that color, shape, and text can be read equally well on all sides ofthe ordnance. The non-uniform arrangement for the ATACS inspectionsystem shown in FIG. 10 provides for uniform spacing of bullet profileor edge data sections (eight equally spaced profiles around eachbullet), at the expense of a nonuniform aerial coverage. This is anacceptable trade-off because there is no text marking on small armsmunitions and surface changes due to corrosion tend to be spread overlarge areas of the bullet if they are present and will not be missed inspite of nonuniform aerial resolution.

The hardware arrangement supporting the preferred four (4) camera systemis shown in FIG. 11. The system uses four (4) high resolution, highframe rate, color Atmel AviivA C2 CL 4010 line scan cameras, 4 MatroxMeteor II CameraLink frame grabbers, a standard PC, a set ofilluminators, a user interface display, and a communication interface tomachine control components that sort or divert based on inspectionoutcome. The key to this sensor module is the use of high resolution,line scan cameras. This enables the recognition hardware to be packagedin a small, portable form. Alternative object inspection systems useeither standard or high frame-rate area cameras.

The line scan hardware has two major advantages over the use of the moretraditional area cameras. First, the line scan hardware allows for amuch smaller inspection area, while still inspecting the entire object.For example, when inspecting 120 mm mortar shells, a traditional areascan camera needs to view the entire length of the round at one time(roughly 2 feet). However, the inspection area for the line scan systemis only a few millimeters. For applications where the object is alreadymoving (or can be made to move) the line scan hardware enables thecreation of a much smaller sensor module. Second, the images collectedby the line scan hardware do not blur when the object is in motion. Foran alternative area camera system, the image acquisition must besynchronized with a strobe light to remove unintended blur from theimages of fast moving objects.

Upon completion of the identification and inspection process executed bythe composable machine vision module, each round is classified by typeand by good/bad. If a round is bad it is sorted into a reject bin.Otherwise the round is sorted into a good bin and the bin count isincremented. For the PIDS system, rounds are inserted into thedesignated autoloader slot, and the contents of the slot arecommunicated to the autoloader controller (FIG. 1—autoloader is behindthe PIDS sensor).

For the belt fed ammunition ATACS system, bad rounds need to bede-linked and replaced. This is a complex mechanical operation and isusually performed by manual means. Therefore the belt ATACS systemsimply marks the bad round with paint for later manual intervention(FIG. 2—marker is shown there).

For the bulk feed ATACS system, rounds are dropped through a circularramp to a belt feed-based sorter unit (FIG. 12). Each round passesthrough a laser/LED detector at the beginning of the sorter to mark whenthe round is presented at the beginning of the sorter belt. Sort binsare located at uniform positions along the belt opposite from computercontrolled blow-off air jets. Because the belt is controlled to auniform speed, a round can be sorted to a particular bin by actuatingthe opposing blow-off jet timed from when a round is marked at the inputlaser/LED detector station.

The sorter controller accepts the bin assignment information from thecomposable machine inspection module as the round is falling towards thesorter unit. When the round is detected and marked at the inputLED/laser detector station, a timed task is initiated that will trackthe bullet until it is in position for the blow-off event into theassigned bin. The air jet is actuated for a short pulse period andcompressed air blows the bullet into its assigned bin. The logic of thesorter allows for multiple bullets and blow-off events occurringsimultaneously to maintain a high sorter throughput rate. Diverterapproaches like that described in Linton (U.S. Pat. No. 6,822,181) couldbe alternatively be employed in the sorter.

Two additional bins, one at the end of the belt and one before thebullet has passed through the input laser/LED detector station, isincluded in the sorter (also shown in FIG. 12). The first bin is analternative bad munition divert station. The air jet blow-off for thisbin can be actuated at a timed interval after bullet is detect at thedivert LED/laser sensor. The bin located at the end of the sorter beltis used for bullets that fail to be identified by the inspectionmodule—this is generally a low probability event but can occur due to avariety of inspection algorithm failures that prevent the system fromproperly classifying a particular round as any known type.

The composable machine vision module is based on a ComposableIdentification Framework (CIF). The CIF coordinates and controls all ofthe plug-in software modules (see FIG. 13). This includes the imagecapture hardware driver modules, the recognition modules, the decisionmodule, and any user interface modules. The CIF also manages thecommunication between the inspection system and the sorter equipmentthat relies on its object classification/inspection results (such as theweapon systems mission control system for the PIDS or the bad roundmarking device or sorter/diverter for the ATACS).

The CIF monitors the data captured by the inspection hardware todetermine when an object passes by the line scan cameras. This detectionoccurs through a simple background separation algorithm. Once thepresence of an object is detected, the system assembles four (4) imagesof the object from the line scan data. The CIF collects data from objectmotion detection sensors to determine the speed of the object for eachline in the image. This information is used to properly construct,correct, and subwindow the image for use by the recognition modules.

The CIF then sends these images to the recognition modules to score thenew object against a set of metrics (this can include size, shape,color, intensity, character recognition, corrosion, physical features,RFID, barcode, etc.). The CIF is designed to allow the flexible addition(composition) of new recognition modules. The results from therecognition modules are sent to the decision module(s) that is(are)responsible for determining the identity and quality of the detectedobject through fusion of the results from the multiple recognitionmodules. The CIF sends the result of the decision module to the userinterface or other connected equipment to control sort/divert or otherafter inspection operations.

The CIF system is designed in a modular fashion so that recognitionmodules can be added to or removed from the system. The main purpose forthis is to allow for easy expansion or reconfiguration of therecognition system. This capability is exploited to quickly create newinspection systems from the present embodiment.

The identification and inspection system is composed of individualrecognition modules that can be used to aid in the process or objectidentification and inspection. Existing modules include the following:

Size and Shape Recognition Module: The shape recognition module iscapable determining the 2D profile of the object presented in an imagesubwindowed to include the complete object to be inspected. The profileinformation is collected through an edge detection process that isoptimized to yield accurate subpixel position boundary information underthe optical and uniform illumination conditions present in the PIDS orATACS imaging system. The system is designed to be used in environmentswhere it is difficult to keep the optics clean (industrial applications,military uses in the desert, etc.) Therefore, the edge detectionalgorithm was designed to negate the effects of dust and light dirt onthe optics.

The edge locations are determined in the following way scanning from thesubwindow boundary left to right (to get the left edge) and also fromright to left) to get the right edge):

-   -   (1) form the Sobel operator (FIG. 14).    -   (2) if the magnitude of the Sobel is <−T then an edge has been        identified other move over one pixel and repeat (1).    -   (3) Back up and reevaluate the Sobel operator.    -   (4) If the magnitude of the Sobel is <0 repeat (3)    -   (5) Use the Sobel magnitude at this position and the Sobel        magnitude at the previous position as two points along a line.        Perform linear interpolation to determine the subpixel position        where the Sobel magnitude would=0. This is the best edge        position.

This relatively complex boundary detection algorithm is required toovercome rather substantial boundary position errors generated bysimpler edge location approaches generate due to substantial inspectionobject surface reflectance related variation (from very shiny to verymat).

The shape recognition module matches the collected edge informationagainst stored template data for known objects. If they match within aselectable tolerance, Δ_(identify), then the object has the same shapeas the template object. In most cases, this information alone is notenough to predict the identity of the object. Therefore, the informationis sent to the decision module to be used as part of the decision makingprocess.

The shape recognition module can also be used to detect damage on anobject. Damage is detectable through smaller differences, Δ_(inspect),between the template and the collected edges. These differences can bemapped to an actual defect size based on properly calibrated optics ofthe imaging hardware. Generally:Δ_(identity)>Δ_(inspect).

Finally, the shape recognition module creates boundaries for the otherrecognition modules. The other modules only need to analyze data withinspecific areas of the identified object. The shape recognition modulestores the edge map for the object. The other modules will only analyzedata within the object, as delineated by the edge map.

The shape recognition module creates an overall object size (length,width, etc.) by determining the bounding box of the edge maps.

Hue Color Recognition: The hue color recognition module detects huecolor information. This module detects hue color information in bands,blobs, or average for the entire object. This information is used tofurther identify the object type or to find defects. For example, huecolor is used to identify the model type for ammunition within a caliber(FIG. 15—M855 has a green tip, M856 has an orange tip, etc.). Hue coloris used to find corrosion on an object—Copper Sulfate related corrosiondue to propellant leakage causes corrosion spots with a green bandtint—FIG. 16. The results of this module are sent to the decision modulefor final decision on the object type and quality.

RGB Color Recognition: The RGB color recognition module detects RGBcolor information. This module detects RGB color information in bands,blobs, or average for the entire object. This information is used tofurther identify the object type or to find defects as an alternativecolor model to Hue and Saturation (the Hue Color Recognition module).

Intensity Recognition: The intensity recognition module detectsintensity information in blobs, bands, or average for the object.Intensity information can be used to determine the material type of theobject. For example, this module is used to determine if an ammunitioncartridge is steel (gray) or brass or the bullet tip is silver or copper(FIG. 17). The intensity information is used to determine if an objectis painted a dark color or a light shade. Since the color recognitionmodules attempt to determine color separately from intensity, intensityinformation is necessary to differentiate different shades of aparticular color.

Character Recognition: The character recognition module includes agradient-based algorithm for finding the location of text on an object.Then it employs optical character recognition to identify the charactersin the identified region (FIG. 18). This module is useful for readinginformation such as model numbers and lot numbers that may be includedon an object.

Gradient Recognition: The gradient recognition module examines the imageand detects pixel gradients that are associated with specific types ofdents. The system can be set to flag the gradients based on gradientmagnitude or direction. This module has been used to detect dents on theshoulder area of small arms ammunition—FIG. 19.

Feature Detection: The system includes the ability for compound featuredetection. Typically, the exact feature detection composition depends onwhat is to be detected. For example, we have implemented an algorithmfor determining the number of charges on a 120 mm mortar shell bycounting the number of “charge” features present.

Barcode: The system accommodates a standard barcode reading mechanismfor incorporating commercially defined bar code readers.

RFID: The system accommodates a standard RFID reading mechanism forincorporating commercially defined RFID code readers.

The system can be expanded to include new recognition modules coded assoftware plug-in modules. These can include modules such as, eddycurrent analysis, ultrasound analysis, laser spectroscopy orinterferometry, etc.

The decision module is responsible for compiling the results from all ofthe recognition modules to determine the identity and quality of theobject.

The Sensor Module bundles all of the software modules needed to drivethe image capture hardware devices and associated sensors.

The invention claimed is:
 1. A recycling system, comprising: a conveyorsubsystem; a feeder subsystem for feeding a recycling stream to theconveyor subsystem, wherein the feeder subsystem and the conveyorsubsystem convey objects to be recycled to a machine vision inspectionstage; wherein the machine vision inspection stage includes a pluralityof imaging and profiling cameras for simultaneously capturing image dataand boundary edges or profiles at different angles associated with theobjects to be recycled; an illumination assembly for uniformlyilluminating each object during capture by the cameras in the machinevision inspection stage; at least one processor for analyzing the imagedata and boundary edges or profiles to determine physicalcharacteristics of the objects; and a sorter for directing objects intoone of a plurality of corresponding bins in accordance with the physicalcharacteristics.
 2. The system of claim 1, wherein the objects includeglass or plastic objects.
 3. The system of claim 1, wherein the objectsinclude glass or plastic bottles.
 4. The system of claim 1, where thephysical characteristics include object shape.
 5. The system of claim 1,where the physical characteristics include object size.
 6. The system ofclaim 1, where the physical characteristics include object color.
 7. Thesystem of claim 1, where the physical characteristics include surfacemarkings.
 8. The system of claim 1, where the physical characteristicsinclude text markings.
 9. The system of claim 1, where the physicalcharacteristics include barcodes.
 10. The system of claim 1, where thephysical characteristics include possible defects.
 11. The system ofclaim 1, wherein the side images and profiles are processed to compareobjects to stored object templates.
 12. The system of claim 1, whereinthe objects comprise trash.
 13. The system of claim 1, wherein theobjects fall freely past the cameras.
 14. The system of claim 1, furtherincluding a strobe light for uniformly illuminating the objects duringcapture by the cameras in the machine vision inspection stage.
 15. Thesystem of claim 14, wherein at least one of the cameras and the strobelight are synchronized.
 16. The system of claim 1, including a modulefor performing an eddy current analysis on the objects.
 17. The systemof claim 1, including a module for performing an ultrasound analysis onthe objects.
 18. The system of claim 1, including a module forperforming a laser spectroscopic analysis on the objects.
 19. The systemof claim 1, including a module for performing an interferometricanalysis on the objects.